Monitoring the type and condition of biological samples such as cells, intracellular structures, and biological tissue (e.g., living/dead cell, cell cycle phases) with use of an optical microscope is a key technique used when screening medical agents and variants and when evaluating the effects of chemical substances and environmental variations on an organism. Particularly in recent years, by using fluorescent proteins and vital staining reagents to selectively label specific intracellular structures (various types of organelles, cytoskeletal systems, and the like) and proteins and observe them in an alive state, it has become possible to comprehend the minute morphology of intracellular structures and the locations of various types of proteins over time, thus enabling a detailed understanding of physiological responses to exposure to medical agents, variations in environmental conditions, and the like. Accordingly, the densification of screening conditions and the refinement of biological effect evaluation references has been progressing, and also there has been rapid diversification in biological samples targeted for evaluation and in the targets of labeling. In the fields of both application and basic research, among the steps in the process of evaluating the types and conditions of biological samples, there has been progress in the automation of and an increase in the throughput of the imaging step. Meanwhile, the evaluation of obtained microscope image data is currently mainly performed by labor-intensive manual screening, and there is desire for such screening to be automated and made more efficient.
The automation of evaluating the types and conditions of biological samples (hereinafter, called “biological sample evaluation”) has been mainly approached as a problem with respect to recognizing patterns in microscope images, and such automation has been realized with the use of an image classifier that classifies the types and conditions of captured images of particular biological samples into several known groups. For example, there is an apparatus that calculates feature parameters from image data of imaged cells and classifies the types of the cells with use of the feature parameters (e.g., see Patent Document 1).
Patent Document 1 discloses a cell classification apparatus that includes a parameter calculation unit that calculates feature parameters regarding the color, surface area, and shape of imaged cells from the image data of such cells, and a classification unit that classifies the cells with use of such feature parameters. Patent Document 1 also discloses that the classification unit includes a first reliability calculation unit that, based on the feature parameters, calculates a first degree of reliability indicating that a cell is a first cell, and a second reliability calculation unit that calculates, based on the feature parameters, a second degree of reliability indicating that a cell is a second cell. These first reliability calculation unit and second reliability calculation unit are configured from a neural network that has been trained using the feature parameters acquired by the parameter calculation unit as input.    Patent document 1: JP 2004-340738A